CoRM-RAG Evidence Critic

This repository hosts the released Evidence Critic checkpoint for CoRM-RAG:

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation
Peiyang Liu, Qiang Yan, Ziqiang Cui, Di Liang, Xi Wang, Wei Ye
arXiv: https://arxiv.org/abs/2605.01302

Code: https://github.com/PeiYangLiu/CoRM-RAG

Model Description

CoRM-RAG aligns retrieval with decision safety rather than semantic similarity alone. The Evidence Critic is a lightweight reranking model trained to score whether a document remains useful under cognitively biased query perturbations, such as false premises, confirmation bias, and distracting assumptions.

The released checkpoint uses a microsoft/deberta-v3-large backbone and outputs a robustness score for a (query, document) pair. It is intended to be used inside the CoRM-RAG pipeline for evidence reranking and risk-aware retrieval.

Files

critic-v12-mixed/checkpoint-latest/state.pt

This file is a PyTorch checkpoint consumed by the CoRM-RAG codebase.

Usage

Install the code from GitHub and download the checkpoint:

git clone https://github.com/PeiYangLiu/CoRM-RAG.git
cd CoRM-RAG

huggingface-cli download PeiyangLiu/CoRM-RAG \
  critic-v12-mixed/checkpoint-latest/state.pt \
  --local-dir checkpoints/hf

Run evaluation by pointing CRITIC_PATH to the downloaded checkpoint:

CRITIC_PATH=checkpoints/hf/critic-v12-mixed/checkpoint-latest/state.pt bash src/run_eval.sh

For training-data construction, critic training, and end-to-end evaluation details, see the GitHub repository.

Intended Use

This checkpoint is intended for research on robust retrieval-augmented generation, evidence reranking, and risk-aware retrieval under biased or perturbed user queries. It is not a standalone generative model.

Limitations

The critic score reflects robustness patterns learned from the CoRM-RAG training pipeline and should be interpreted within that retrieval setting. Performance may vary across domains, corpora, retrievers, and perturbation distributions.

Citation

@misc{liu2026cormrag,
  title={Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation},
  author={Peiyang Liu and Qiang Yan and Ziqiang Cui and Di Liang and Xi Wang and Wei Ye},
  year={2026},
  eprint={2605.01302},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2605.01302}
}